The substantial computational cost of high-fidelity models in numerical hemodynamics has, so far, relegated their use mainly to offline treatment planning. New breakthroughs in data-driven architectures and optimization techniques for fast surrogate modeling provide an exciting opportunity to overcome these limitations, enabling the use of such technology for time-critical decisions. We discuss an application to the repair of multiple stenosis in peripheral pulmonary artery disease through either transcatheter pulmonary artery rehabilitation or surgery, where it is of interest to achieve desired pressures and flows at specific locations in the pulmonary artery tree, while minimizing the risk for the patient. Since different degrees of success can be achieved in practice during treatment, we formulate the problem in probability, and solve it through a sample-based approach. We propose a new offline-online pipeline for probabilsitic real-time treatment planning which combines offline assimilation of boundary conditions, model reduction, and training dataset generation with online estimation of marginal probabilities, possibly conditioned on the degree of augmentation observed in already repaired lesions. Moreover, we propose a new approach for the parametrization of arbitrarily shaped vascular repairs through iterative corrections of a zero-dimensional approximant. We demonstrate this pipeline for a diseased model of the pulmonary artery tree available through the Vascular Model Repository.

翻译：高保真数值血流动力学模型计算成本高昂，迄今主要局限于离线治疗计划。数据驱动架构与快速代理模型优化技术的新突破为克服这些局限提供了激动人心的机遇，使此类技术可用于时间关键型决策。我们讨论其在经导管肺动脉修复术或外科手术修复外周肺动脉疾病中多处狭窄的应用，其目标是在降低患者风险的同时，在肺动脉树的特定位置实现期望的压力与血流。由于治疗过程中实际可达到的成功程度存在差异，我们将问题以概率形式表述，并通过基于样本的方法求解。我们提出一种新的离线-在线概率实时治疗计划流程，该流程结合了边界条件的离线同化、模型降阶及训练数据集生成，以及边缘概率的在线估计（可能以已修复病变观测到的扩张程度为条件）。此外，我们还提出了一种新方法，通过零维近似解的迭代校正对任意形状的血管修复进行参数化。我们使用血管模型库中提供的病变肺动脉树模型演示了这一流程。